Method and system for saving database storage space

ABSTRACT

Saving database storage space includes extracting a standard property unit from a database of commodity information and including the SPU in a SPU library, generating a sequence document of the standard property unit and sending the sequence document to a front-end device, determining whether a newly released commodity matches the standard property unit of the sequence document of the standard property unit and in the event that the newly released commodity matches the standard property unit of the sequence document, binding the new released commodity and the matched standard property unit.

CROSS REFERENCE TO OTHER APPLICATIONS

This application claims priority to People's Republic of China PatentApplication No. 200810147054.5 entitled METHOD AND SYSTEM FOR SAVINGDATABASE STORAGE SPACE filed Aug. 13, 2008 which is incorporated hereinby reference for all purposes.

FIELD OF THE INVENTION

The present invention relates generally to the field of computer networktechnology and more particularly to method and system for savingdatabase storage space.

BACKGROUND OF THE INVENTION

With the development of computer network technology and the boom ofonline shopping, a lot of shopping sites have appeared. Sellerstypically register with a shopping site, upload commodity informationand complete the transactions at home.

For example, presently the shopping site taobao.com has 180 millionitems for sale (also referred to as commodities). Each commodityreleased by a seller has its corresponding name, picture, briefdescription, price range and related properties. Since the same productmay be released by multiple sellers as multiple commodities, there is alot of redundancy in the data of these commodities. Take the Nokia N73mobile phone for example, each time a seller releases a Nokia N73 phoneas a commodity, the corresponding picture(s), description of theproduct, the functions and other information are entered by the sellerand stored. When a large number of sellers are selling the same mobilephone, a great deal of duplicate data is stored. Since the typicalshopping sites use databases to store the commodity entries, maintaininga large number of commodities in the database can be expensive.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a flowchart illustrating an embodiment of a process for savingdatabase storage space.

FIG. 2 is a flowchart illustrating another embodiment of a process forsaving database storage space.

FIG. 3 is a flowchart illustrating an example process for savingdatabase storage space.

FIG. 4 is a flowchart illustrating another example of a process forsaving database storage space.

FIG. 5 is a system diagram illustrating an embodiment of a system forsaving database storage space.

FIG. 6 is a system diagram showing an embodiment of a DB.

FIG. 7 is a block diagram illustrating an embodiment of a SPU server.

FIG. 8 is a block diagram illustrating an embodiment of a back-endserver.

FIG. 9 is a block diagram illustrating an embodiment of a front-endserver.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

FIG. 1 is a flowchart illustrating an embodiment of a process for savingdatabase storage space.

In the process shown, at 101, a formal standard property unit (SPU) isextracted from commodity information and is added to a SPU library. Asused herein, a SPU refers to an aggregation of a set of one or moreproducts that share a set of one or more identical properties. In someembodiments, a SPU is implemented as a collection of a group of commonproperties of a back-end standard category leaf node. In someembodiments, a SPU has the following properties: a name, a briefdescription, one or more pictures, a price, one or more main propertiesand one or more binding properties and an association with other SPUs.Among a multitude of commodities, a server can classify the commoditieswith common properties together to form a SPU. The SPU is saved in theform of a data table that includes the SPU's ID and the data table isassociated with a list of categories using the ID, to represent thecategory that a SPU belongs to, so that all SPUs under a category canfound. SPUs can be re-used.

In some embodiments, extracting the SPU and including it in a SPUlibrary includes: search the key properties of the commodity incommodity information; based at least in part on the found keyproperties of the commodity, identify common properties of the commodityusing techniques such as data mining of database; based at least in parton the common properties of the commodity, use cluster analysis toextract a candidate SPU that includes common properties of thecommodity; verify whether the candidate SPU indeed includes the commonproperties of the commodity; if verified, generating a formal SPU andincluding it in a SPU library.

At 102, based on the SPUs in the SPU library, a SPU sequence document isgenerated and the sequence document is sent to the front endperiodically according to predetermined time intervals.

As used herein, a sequence document of a SPU is a collection of the datain the database, including the category from the back-end, the SPU, SPUsthat are associated with this SPU and other appropriate information. Forexample, in the sequence document of SPU for the mobile phone Nokia N73,the back-end category is mobile phone and a SPU can be found for thecategory. The SPU includes the following information: the brand isNokia, the model is N73 and the operating system is Symbian. Other SPUsassociated with this SPU can also be found (such as the SPU of thebattery of Nokia N73 or other accessories of this mobile phone model).

In some embodiments, generating a sequence document of a SPU includes:acquiring category information from the back-end categories, acquiringall the SPUs of the category by querying the SPU library using theacquired category information and generating the sequence document usingthe acquired SPUs.

At 103, it is determined whether a newly released commodity matches theSPU in the sequence document of SPU. If so, the new released commodityand the matching SPU are bound. The binding of the new releasedcommodity and the matching SPU includes: establishing a correspondingrelationship of the new released commodity and the matching SPU andsaving the corresponding relationship for subsequent operations.

If the newly released commodity matches a SPU in a sequence document,the corresponding relationship of the matching SPU and the new releasedcommodity is established. Any mismatched properties that are deemed tobe the special properties of the commodity are saved in the field ofproperty.

In some embodiments, database mining techniques such as cluster analysisare applied to a back-end category of commodities having the sameproperties. The common properties of commodities are extracted and areintroduced into the SPU to indicate the common properties of thecommodities. If a newly released commodity matches a sequence documentof a SPU, then the commodity and the matching SPU in the sequencedocument are bound. Instead of separately tracking billions ofcommodities, only millions of SPUs need to be tracked. Thus, storagespace is saved and commodities are conveniently standardized. Inaddition, by using the SPU pictures, it can save picture storage spaceand reduce storage costs and network traffic.

FIG. 2 is a flowchart illustrating another embodiment of a process forsaving database storage space.

In this example, at 201, the key properties of a commodity are searchedin a database. In some embodiments, a search engine is used topre-organize and index the database. The search engine collects millionsto billions of commodities and indexes the combination of each propertyand property value of commodities to establish a full-text search engineof the index database. When searching for a certain main property, allthe commodity properties including the combination of the property andproperty value will be found as search results.

At 202, using to the key properties found in 201, common properties ofthe commodity are determined. In some embodiments, database miningtechniques are used to determine the common properties of the commodity.

Data mining is used to extract the knowledge people are interested infrom the data of large database. The knowledge is connotative, unknownin advance and is potentially useful information. The knowledgeextracted is represented to be concepts, rules, regularities, patternsand other forms. This definition also defines the subject of data miningas a database and, generally speaking, data mining is a decision-makingsupport process to find out a pattern in a number of facts orcollections of observational data. The subject of data mining is notonly a database, but also can be a file system or any other datacollection organized together. The ultimate goal of data mining is todiscover the connotative mode which cannot be detected easily. Generallyspeaking, the easiest mode to be understood among these patterns is thestatistical model. Secondly, they is the external detection of data, therecognition, classification or clustering of a large-scale datacollection. Finally, it is to solve the problems related tomulti-dimensional space and huge data processing in a majority ofdatabase management systems from the theory and calculating aspects.

After the search engine has found the key properties of commodities fromthe database, the data mining techniques for the database is used tofurther mine the properties of the commodities from the database andcalculate the common properties of the commodities. The commonproperties of the commodities mentioned here refer to the regularity orthe rule [if then] existing among different key properties values amonga large amount of data.

At 203, based on the common properties of the commodity determined in202, cluster analysis is applied to commodities with the same kind ofproperty within the same backend category and a candidate SPU isextracted to be verified with the common properties of the commodity.

As used herein, a back-end category refers to the type of commodity,such as mobile phone, mp3 and so on. The data structure of a back-endcategory includes a table of the standard list of category fromback-end, related properties of commodities and related SPUs.

At 204, the candidate SPU is verified. The candidate SPU extracted in203 may not be exact and should to be verified by a human to judgewhether the candidate SPU indeed has the common properties of thecommodity. If verification succeeds, a formal SPU is extracted and addedinto a SPU library.

At 205, the formal SPU is calculated to generate a sequence document ofthe SPU and the sequence document of the SPU is sent to the front-endperiodically.

At 206, it is determined whether the newly released commodity matchesthe sequence document of the SPU sent periodically in 205. When a sellerreleases a new commodity, the seller is asked to select the propertiesof the commodity. A user interface with property choices is provided insome embodiments. The properties the seller chose are compared with theSPU sequence document which is sent to the front-end periodically. Ifthere is a match, the commodity and the matching SPU are bound. Thenot-matched properties are considered to be special properties of thecommodity and are saved in the field of property of the commodity.

FIG. 3 is a flowchart illustrating an example process for savingdatabase storage space.

At 301, the key properties of the commodity in a database are searchedautomatically by a search engine. Take searching for mobile phone NokiaN73 for example. The properties of mobile phone Nokia N73 found by asearch engine include: the brand is Nokia, the model is N73 and theoperating system is Symbian.

At 302, according to the key properties searched in step 301, usingdatabase mining techniques to mine the common properties of thecommodity. For example, the property collection of mobile phone NokiaN73 is obtained by searching. Calculating the searched key properties ofmobile phone Nokia N73 by database mining techniques and extracting thecommon properties of mobile phone Nokia N73: the brand is Nokia, themodel is N73, the memory card is mini SD and the operating system isSymbian and so on.

At 303, according to the common properties of the commodity found in302, a SPU is extracted and verified with the common properties of thecommodity by applying cluster analysis to the commodities with the samekind of properties within the back-end categories. In this case, theback-end category is mobile phone. According to the common properties ofthe commodity determined by database mining techniques, the commonproperties of mobile phone Nokia N73 are extracted by using clusteranalysis and other techniques. A candidate SPU is generated and ready tobe verified. Other related properties of mobile phone Nokia N73 (such asmobile phone accessories, etc.) generate related candidate SPUs to beverified.

At 304, the candidate SPU is verified and a formal SPU is approved. TheSPU obtained in 303 may be not exact and should be verified by a humanto determine whether the SPU indeed has the common properties of thecommodity. If the verification is successful, a formal SPU is generatedand put into a SPU library.

At 305, the formal SPU is used to generate a sequence document of SPUand the sequence document is sent to the front-end periodically.

At 306, it is determined whether a newly released commodity matches thesequence document sent periodically in step 305. When a seller releasesnew commodity, the seller chooses the properties of the commodity via auser interface. The properties chosen by the seller and the sequencedocument sent periodically are compared. If there is a match, thecommodity and the matched SPU are bound. The unmatched properties areconsidered to be special properties of the commodity and are saved inthe field of property of the commodity. Thus, if a newly releasedcommodity by the seller is mobile phone Nokia N73, this commodity isbound to the SPU of mobile phone Nokia N73. Moreover, the SPU can beassociated with related SPUs (such as the SPU of battery for mobilephone Nokia N73).

FIG. 4 is a flowchart illustrating another example of a process forsaving database storage space. In this example, the process includes thefollowing:

At 401, the key properties of the certain commodities are searched in adatabase, preferably by a search engine. For example, mobile phonesNokia N73, N72 and N76 are searched using a search engine and theproperties of these mobile phones are obtained, including the brand, theoperating system, the type of memory card, the ring type, any advancedfeatures, number of pixels and so on.

At 402, according to the key properties found in 401, using the databasemining techniques to determine the common properties of the commodity.For example, the search obtains a collection of various properties formobile phones Nokia N73, N72 and N76. Based on the collection ofproperties, data mining techniques are applied and the common propertiesof mobile phones Nokia N73, N72 and N76 are extracted.

At 403, based on the common properties of the commodity determined in402, cluster analysis is applied to back-end categories that have thesame kinds of properties to extract candidate SPUs that have the samecommon properties. In this case, based on the common propertiesdetermined by database mining techniques, the common properties ofmobile phones Nokia N73, N72 and N76 are extracted by cluster analysisand other techniques to generate candidate SPUs to be verified. Therelated properties (such as mobile phone accessories, etc.) generaterelated SPUs to be verified for mobile phones Nokia N73, N72 and N76.

At 404, the candidate SPUs are verified and formal SPUs are extracted ifverifications are successful. Since the candidate SPUs obtained in 403may be not exact, they should be verified by human to determine whetherthe SPUs have the common properties of the commodities. If so, formalSPUs for mobile phone Nokia N73, N72 and N76 are generated respectivelyand added to the SPU library.

At 405, the SPUs obtained in 404 are fine-grained SPUs which all havethe common properties. The common properties of Nokia N73, N72 and N76are calculated to acquire the SPU of mobile phone Nokia Series N, inwhich a coarse-grained SPU which includes fine-grained SPUs for theindividual models.

At 406, formals SPUs are used to generate sequence documents of theSPUs. The sequence documents are sent to the front-end periodically.

At 407, it is determined whether a newly released commodity matches thesequence documents. When a seller releases a new commodity, the sellerchooses the properties of the commodity via a user interface. Theproperties chosen by the seller and the sequence documents sentperiodically are compared. If there is a match, the commodity and thematching SPU are bound. Any unmatched properties are considered specialproperties of the commodity and are saved in the field of property ofthe commodity.

FIG. 5 is a system diagram illustrating an embodiment of a system forsaving database storage space. System 500 shown in this example may beused to implement the processes described above in connection with FIGS.1-4. In this example, system 500 includes a database (DB) 510, a SPUserver 520, a back-end server 530 and a front-end server 540.

DB 510 is used for storing commodity information and formal SPUs. Forexample, on the shopping site Taobao, when a seller releases newcommodities, each commodity corresponds to a name, picture, briefdescription, price range, related properties and other information whichare stored in DB 510. The formal SPUs from the server 520 are alsostored in DB 510.

In some embodiments, DB 510 is a data collection organized according tosome data model and stored in a second-level memory. That datacollection has the following characteristics: it is highlynon-repetitive, serves for a variety of applications of a certainorganization optimally, employs data structures that are independent ofthe application program and the adding, deleting, changing and searchingof data is managed and controlled by uniform software.

SPU server 520 is configured to cache the data from DB 510 and back-endserver 530, such as the formal SPUs and commodity properties from DB 510and the formal SPUs from back-end server 530. The formal SPU iscalculated to generate a sequence document of SPU. The sequence documentof SPU is sent to front-end server 540 periodically under the control ofa set program. The commodity properties are sent to back-end server 530.The formal SPU generated by back-end server 530 are sent to DB 510.

Back-end server 530 is configured to generate automatically a candidateSPU to be verified by program according to the commodity properties fromthe server 520 and generating a formal SPU by operating verification.Back-end server 530 searches for the commodity properties from theserver 520 by a search engine, extracts the key properties of commodity,mines the common properties of commodity by using database miningtechniques and applies cluster analysis to the common properties,generates a SPU to be verified with the common properties of thecommodity and verifies whether the SPU is the common properties of thecommodity. If so, back-end server 530 generates a formal SPU and sendsit to SPU server 520.

Front-end server 540 is configured to receive the SPU sequence documentsent by SPU server 520 periodically and to match the newly releasedcommodity with the SPU in the sequence document. Front-end server 540receives and saves the sequence documents sent by SPU server 520periodically. When a seller releases a new commodity, the seller firstchooses the properties of the commodity via a user interface. Front-endserver 540 compares the properties chosen by the seller and the SPU inthe sequence document of SPU stored in the front-end server 540 todetermine whether there is a match between the commodity and the SPU.The unmatched properties are considered the special properties of thecommodity and are saved in the field of property of the commodity.

FIG. 6 is a system diagram showing an embodiment of a DB such as 510. Inthis example, the DB is shown to include a first storing module 511 anda second storing module 512.

The first storing module 511 is configured to store the formal SPU withthe common properties of the commodity from SPU server 520.

The second storing module 512 is configured to store all commodityinformation.

FIG. 7 is a system diagram illustrating an embodiment of a SPU serversuch as 520. In this example, the SPU server includes a receiving module521, a calculating module 522, a controlling module 523, a first sendingmodule 524 and a second sending module 525.

The receiving module 521 is configured to receive the formal SPU andcommodity information from DB 510 and the formal SPU from the secondsending module 525. The formal SPU from DB 510 is sent to thecalculating module 522, the commodity information from DB 510 is sent tothe second sending module 525 and the formal SPU from the second sendingmodule 525 is sent to DB 510.

The calculating module 522 configured to calculate based the formal SPUfrom the receiving module 521 to generate a sequence document of SPU andto send the sequence document of SPU to the first sending module 524.

The first sending module 524 is configured to send the sequence documentof SPU from the calculating module 522 under the control of thecontrolling module 524.

The second module 525 is configured to send commodity information toback-end server 530 and receiving the formal SPU generated by back-endserver 530.

The control module 523 is configured to control the first sending module524 to send a sequence document of SPU periodically by setting the timeinterval for sending the sequence document of SPU.

FIG. 8 is a block diagram illustrating an embodiment of a back-endserver such as 530. In the example shown, the back-end server comprisesan accessing module 531, a searching module 532, a mining module 533, aclustering module 534 and an operating module 535.

The access module 531 is configured to access commodity information fromDB 510 and to send commodity information to the searching module 532.

The search module 532 is configured to search for the commodityproperties from accessing module 531 to get the key properties of thecommodity.

The mining module 533 configured to receive the key properties of thecommodity searched by searching module 532, and to extract the commonproperties of the commodity by using database mining techniques.

The clustering module 534 is configured to apply cluster analysis to thecommon properties of the commodity extracted by the mining module 533and to generate a SPU to be verified.

The operating module 535 is used to receive the SPU to be verifiedgenerated by the clustering module 534, to verify the SPU by theoperating module 535 and to determine whether the SPU to be verified hasthe common properties of the commodities. If so, the operating modulealso generates a formal SPU and sends the formal SPU to SPU server 520.

FIG. 9 is a block diagram illustrating an embodiment of a front-endserver such as 540. In this example, the front-end server includes areceiving module 541, a determining module 542, a binding module 543 anda merging module 544.

The receiving module 541 is configured to receive the sequence documentof SPU and the commodity information released when the seller releasescommodity from SPU server 520, including the list of categories itbelongs to, properties and so on.

The determining module 542 is configured to determine whether thereleased commodity properties from the receiving module 541 match theSPU in the sequence document of SPU.

The binding module 543 is configured to bind the commodity and thesequence document of SPU according to the result judged by determiningmodule 542. If there is a match, the commodity and the matching SPU arebound.

The merging module 544 is configured to merge the standard propertiesfrom the SPU bound by binding module 543 and the personal properties ofthe commodity and to display to a buyer when the buyers view thecommodity details.

The modules described above can be implemented as software componentsexecuting on one or more general purpose processors, as hardware such asprogrammable logic devices and/or Application Specific IntegratedCircuits designed to perform certain functions or a combination thereof.In some embodiments, the modules can be embodied by a form of softwareproducts which can be stored in a nonvolatile storage medium (such asCD-ROM, U disk, mobile hard disk, etc.), including a number ofinstructions for making a computer device (such as personal computers,servers, network equipments, etc.) implement the methods described inthe embodiments of the present invention. The modules may be implementedon a single device or distributed across multiple devices. The functionsof the modules may be merged into one another or further split intomultiple sub-modules.

Using SPUs to represent the common properties of the commodities cansave storage space and regulate commodities conveniently, as well assave the picture storage space, reduce storage costs and network trafficby using the pictures from the SPU.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A method comprising: extracting a standardproperty unit from a database of commodity information and include theSPU in a SPU library, wherein the standard property unit comprises oneor more properties common to one or more products associated with aproduct category; generating a sequence document of the standardproperty unit and sending the sequence document to a front-end device,wherein the sequence document of the standard property unit comprisesdata associated with the standard property unit including an identifierassociated with the product category and at least one related standardproperty unit; determining whether a newly released commodity matchesthe standard property unit of the sequence document of the standardproperty unit including by comparing one or more properties of the newlyreleased commodity to the sequence document of the standard propertyunit; and in the event that the newly released commodity matches thestandard property unit of the sequence document, binding the newlyreleased commodity and the matched standard property unit.
 2. The methodof claim 1, wherein extracting the standard property unit comprises:searching for and locating a key property of a commodity in the databaseof the commodity information; based on the key property of thecommodity, determining a common property of the commodity; based on thecommon property of the commodity, generating a candidate standardproperty unit to be verified with the common property of the commodity;verifying whether the candidate standard property unit includes commonproperties of the commodity; in the event that the candidate standardproperty unit is verified, approving the standard property unit to aformal standard property unit, wherein the formal standard property unitcomprises a candidate standard property unit that has been verified. 3.The method of claim 2, wherein determining the common property of thecommodity includes performing database mining.
 4. The method of claim 2,wherein generating the candidate standard property unit includesperforming cluster analysis.
 5. The method of claim 2, wherein thecandidate standard property unit to be verified comprises the standardproperty unit and a related property unit of a related property of thecommodity.
 6. The method of claim 1, wherein the formal standardproperty unit includes a coarse-grained standard property unit thatfurther includes a fine-grained standard property unit.
 7. A systemcomprising: one or more processors configured to: extract a standardproperty unit from a database of commodity information and include theSPU in a SPU library, wherein the standard property unit comprises oneor more properties common to one or more products associated with aproduct category; generate a sequence document of the standard propertyunit and sending the sequence document to a front-end device, whereinthe sequence document of the standard property unit comprises dataassociated with the standard property unit including an identifierassociated with the product category and at least one related standardproperty unit; determine whether a newly released commodity matches thestandard property unit of the sequence document of the standard propertyunit including by comparing one or more properties of the newly releasedcommodity to the sequence document of the standard property unit; and inthe event that the newly released commodity matches the standardproperty unit of the sequence document, bind the newly releasedcommodity and the matched standard property unit; and a memory coupledto the one or more processors, configured to provide the one or moreprocessors with instructions.
 8. The system of claim 7, wherein the oneor more processors are configured to extract the standard property unit,including by: searching for and locating a key property of a commodityin the database of the commodity information; based on the key propertyof the commodity, determining a common property of the commodity; basedon the common property of the commodity, generating a candidate standardproperty unit to be verified with the common property of the commodity;verifying whether the candidate standard property unit includes commonproperties of the commodity; in the event that the candidate standardproperty unit is verified, approving the standard property unit to aformal standard property unit, wherein the formal standard property unitcomprises a candidate standard property unit that has been verified. 9.The system of claim 8, wherein the one or more processors are configuredto determine the common property of the commodity, including byperforming database mining.
 10. The system of claim 8, whereingenerating the candidate standard property unit includes performingcluster analysis.
 11. The system of claim 8, wherein the candidatestandard property unit to be verified comprises the standard propertyunit and a related property unit of a related property of the commodity.12. The system of claim 7, wherein the formal standard property unitincludes a coarse-grained standard property unit that further includes afine-grained standard property unit.
 13. A system comprising: a databaseconfigured to store commodity information and formal standard propertyunits; a standard property unit server configured to: cache data fromthe database and a back-end server, the data including at least some ofthe formal standard property units and at least some commodityproperties from the database and at least some of the formal standardproperty units from a back-end server, wherein the formal standardproperty unit comprises one or more properties common to one or moreproducts associated with a product category; and generate a sequencedocument for each standard property unit, wherein the sequence documentof the formal standard property unit comprises data associated with theformal standard property unit including an identifier associated withthe product category and at least one related standard property unit; aback-end server configured to generate the formal standard propertyunits based on the commodity properties from the standard property unitserver; a front-end server configured to: receive the sequence documentof a standard property unit from the standard property unit server;match a newly released commodity and a standard property unit includingby comparing one or more properties of the newly released commodity tothe sequence document of the formal standard property unit.